Most applications and documents are stored in a public cloud for storage and management purposes in a cloud computing environment. The major advantages of storing applications and documents in public cloud are lower cost through use of shared computing resources and no upfront infrastructure costs. However, in this case the management of data and other services is insecure. Therefore, security is a major problem in a public cloud as the cloud and the network are open to many other users. In order to provide security, it is necessary for data owners to store their data in the public cloud in a secure way and to use an appropriate access control scheme. Designing a computation and communication efficient key management scheme to selectively share documents based on fine-grained attribute-based access control policies in a public cloud is a challenging task. There are many existing approaches that encrypt documents prior to storage in the public cloud: These approaches use different keys and a public key cryptographic system to implement attribute-based encryption and/or proxy re-encryption. However, these approaches do not efficiently handle users joining and leaving the system when identity attributes and policies change. Moreover, these approaches require keeping multiple encrypted copies of the same documents, which has a high computational cost or incurs unnecessary storage costs. Therefore, this project focused on the design and development of an efficient key management scheme to allow the data owner to store data in a cloud service in a secure way. Additionally, the proposed approach enables cloud users to access the data stored in a cloud in a secure way. Many researchers have proposed key management schemes for wired and wireless networks. All of these existing key management schemes differ from the key management schemes proposed in this thesis. First, the key management scheme proposed in this thesis increases access level security. Second, the proposed key management scheme minimizes the computational complexity of the cloud users by performing only one mathematical operation to find the new group key that was computed earlier by the data owner. In addition, this proposed key management scheme is suitable for a cloud network. Third, the proposed key distribution and key management scheme utilizes privacy preserving methods, thus preserving the privacy of the user. Finally, a batch key updating algorithm (also called batch rekeying) has been proposed to reduce the number of rekeying operations required for performing batch leave or join operations. The key management scheme proposed in this thesis is designed to reduce the computation and communication complexity in all but a few cases, while increasing the security and privacy of the data. / De flesta program och dokument lagras i ett offentligt moln för lagring och hantering ändamål i en molnmiljö. De stora fördelarna med att lagra program och dokument i offentliga moln är lägre kostnad genom användning av delade datorresurser och ingen upfront infrastruktur costs.However, i detta fall hanteringen av data och andra tjänster är osäker. Därför är säkerhet ett stort problem i en offentlig moln som molnet och nätverket är öppna för många andra användare. För att ge trygghet, är det nödvändigt för dataägare att lagra sina data i det offentliga molnet på ett säkert sätt och att använda en lämplig åtkomstkontroll schema. Utforma en beräkning och kommunikation effektiv nyckelhantering system för att selektivt dela dokument som grundar sig på finkorniga attributbaserad åtkomstkontroll politik i en offentlig moln är en utmanande uppgift. Det finns många befintliga metoder som krypterar dokument före lagring i det offentliga molnet: Dessa metoder använder olika tangenter och en publik nyckel kryptografiskt system för att genomföra attributbaserad kryptering och / eller proxy re-kryptering. Dock har dessa metoder inte effektivt hantera användare som ansluter och lämnar systemet när identitetsattribut och politik förändras. Dessutom är dessa metoder kräver att hålla flera krypterade kopior av samma dokument, som har en hög beräkningskostnad eller ådrar sig onödiga lagringskostnader. Därför fokuserade projektet på design och utveckling av en effektiv nyckelhantering system för att möjliggöra dataägaren att lagra data i en molntjänst på ett säkert sätt. Dessutom, den föreslagna metoden gör det möjligt för molnanvändare att få tillgång till uppgifter lagras i ett cloud på ett säkert sätt. Många forskare har föreslagit viktiga förvaltningssystem för fasta och trådlösa nätverk. Alla dessa befintliga system ke, skiljer sig från de centrala förvaltningssystemen som föreslås i denna avhandling. Först föreslog nyckelhanteringssystemet i denna avhandling ökar Medverkan nivå säkerhet. För det andra, minimerar den föreslagna nyckelhanteringssystemet beräkningskomplexiteten för molnanvändare genom att utföra endast en matematisk operation för att hitta den nya gruppknapp som tidigare beräknades av dataägaren. Dessutom är denna föreslagna nyckelhanteringsschema lämpligt för ett moln nätverk. För det tredje, den föreslagna nyckeldistribution och nyckelhantering systemet utnyttjar integritets bevara metoder och därmed skydda privatlivet för användaren. Slutligen har ett parti viktig uppdatering algoritm (även kallad batch nya nycklar) föreslagits för att minska antalet Ny serieläggning av operationer som krävs för att utföra batch ledighet eller gå med i verksamheten. Nyckelhanteringssystemet som föreslås i denna avhandling är utformad för att minska beräknings-och kommunikations komplexitet i alla utom ett fåtal fall, och samtidigt öka säkerheten och integriteten av uppgifterna.

Thesis (M.Ed. (Language Education)) -- University of Limpopo, 2022 / The main purpose of this study was to determine challenges that are faced by Grade 8 learners when reading short stories with understanding using EFAL. The study made use of a research approach which is qualitative in nature to explore the reading challenges. It also used an interpretive paradigm. Schema Reading Theory was employed as the guide to which the study followed. This theory advocates that learners read a text having prior knowledge, which assists them to interpret it better. This chosen theory is relevant to the study as the study seeks to understand why learners cannot read. Therefore the theory assisted in determining whether learners have the necessary prior knowledge of the text they read in the classroom. The main findings of the study has indicated that learners have challenges to reading. There are recommendations addressed to learners, parents, teachers and department of education. In conclusion, teachers assured to initiate reading programmes such as focused, independent and guided reading, with the hope to curb challenges towards reading.

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The SAP HANA database extends the scope of traditional database engines as it supports data models beyond regular tables, e.g. text, graphs or hierarchies. Moreover, SAP HANA also provides developers with a more fine-grained control to define their database application logic, e.g. exposing specific operators which are difficult to express in SQL. Finally, the SAP HANA database implements efficient communication to dedicated client applications using more effective communication mechanisms than available with standard interfaces like JDBC or ODBC. These features of the HANA database are complemented by the extended scripting engine–an application server for server-side JavaScript applications–that is tightly integrated into the query processing and application lifecycle management. As a result, the HANA platform offers more concise models and code for working with the HANA platform and provides superior runtime performance. This paper describes how these specific capabilities of the HANA platform can be consumed and gives a holistic overview of the HANA platform starting from query modeling, to the deployment, and efficient execution. As a distinctive feature, the HANA platform integrates most steps of the application lifecycle, and thus makes sure that all relevant artifacts stay consistent whenever they are modified. The HANA platform also covers transport facilities to deploy and undeploy applications in a complex system landscape.

Data Warehouses (DW) have to continuously adapt to evolving business requirements, which implies structure modification (schema changes) and data migration requirements in the system design. However, it is challenging for designers to control the performance and cost overhead of different schema change implementations. In this paper, we demonstrate SCIT, a tool for DW designers to test and implement different logical design alternatives in a two-fold manner. As a main functionality, SCIT translates common DW schema modifications into directly executable SQL scripts for relational database systems, facilitating design and testing automation. At the same time, SCIT assesses changes and recommends alternative design decisions to help designers improve logical designs and avoid common dimensional modeling pitfalls and mistakes. This paper serves as a walk-through of the system features, showcasing the interaction with the tool’s user interface in order to easily and effectively modify DW schemata and enable schema change analysis.

[pt] O Resource Description Framework (RDF) foi adotado como uma recomendação do W3C em 1999 e hoje é um padrão para troca de dados na Web. De fato, uma grande quantidade de dados foi convertida em RDF, muitas vezes em vários conjuntos de dados fisicamente distribuídos ao longo de diferentes localizações. A linguagem de consulta SPARQL (sigla do inglês de SPARQL Protocol and RDF Query Language) foi oficialmente introduzido em 2008 para recuperar dados RDF e fornecer endpoints para consultar fontes distribuídas. Uma maneira alternativa de acessar conjuntos de dados RDF é usar consultas baseadas em palavras-chave, uma área que tem sido extensivamente pesquisada, com foco recente no conteúdo da Web. Esta dissertação descreve uma estratégia para compilar consultas baseadas em palavras-chave em consultas SPARQL federadas sobre conjuntos de dados RDF distribuídos, assumindo que cada conjunto de dados RDF tem um esquema e que a federação tem um esquema mediado. O processo de compilação da consulta SPARQL federada é explicado em detalhe, incluindo como computar o conjunto de joins externos entre as subconsultas locais geradas, como combinar, com a ajuda de cláusulas UNION, os resultados de consultas locais que não têm joins entre elas, e como construir a cláusula TARGET, de acordo com a composição da cláusula WHERE. Finalmente, a dissertação cobre experimentos com dados do mundo real para validar a implementação. / [en] The Resource Description Framework (RDF) was adopted as a W3C recommendation in 1999 and today is a standard for exchanging data in the Web. Indeed, a large amount of data has been converted to RDF, often as multiple datasets physically distributed over different locations. The SPARQL Protocol and RDF Query Language (SPARQL) was officially introduced in 2008 to retrieve RDF datasets and provide endpoints to query distributed sources. An alternative way to access RDF datasets is to use keyword-based queries, an area that has been extensively researched, with a recent focus on Web content. This dissertation describes a strategy to compile keyword-based queries into federated SPARQL queries over distributed RDF datasets, under the assumption that each RDF dataset has a schema and that the federation has a mediated schema. The compilation process of the federated SPARQL query is explained in detail, including how to compute a set of external joins between the local subqueries, how to combine, with the help of the UNION clauses, the results of local queries which have no external joins between them, and how to construct the TARGET clause, according to the structure of the WHERE clause. Finally, the dissertation covers experiments with real-world data to validate the implementation.

The main aim of this work is to study the thermal conductivity of base fluid with mild inclusion of nanoparticles. We perform numerical study for transportation of Maxwell nanofluids with activation energy and Cattaneo–Christov flux over an extending sheet along with mass transpiration. Further, bioconvection of microorganisms may support avoiding the possible settling of nanoentities. We formulate the theoretical study as a nonlinear coupled boundary value problem involving partial derivatives. Then ordinary differential equations are obtained from the leading partial differential equations with the help of appropriate similarity transformations. We obtain numerical results by using the Runge–Kutta fourth-order method with shooting technique. The effects of various physical parameters such as mixed convection, buoyancy ratio, Raleigh number, Lewis number, Prandtl number, magnetic parameter, mass transpiration on bulk flow, temperature, concentration, and distributions of microorganisms are presented in graphical form. Also, the skin friction coefficient, Nusselt number, Sherwood number, and motile density number are calculated and presented in the form of tables. The validation of numerical procedure is confirmed through its comparison with the existing results. The computation is carried out for suitable inputs of the controlling parameters.

In the recent past, different multidimensional data models were introduced to model OLAP (‘Online Analytical Processing’) scenarios. Design problems arise, when the modeled OLAP scenarios become very large and the dimensionality increases, which greatly decreases the support for an efficient ad-hoc data analysis process. Therefore, we extend the classical multidimensional model by grouping functionally dependent attributes within single dimensions, yielding in real orthogonal dimensions, which are easy to create and to maintain on schema design level. During the multidimensional data analysis phase, this technique yields in nested data cubes reflecting an intuitive two-step navigation process: classification-oriented ‘drill-down’/ ‘roll-up’ and description-oriented‘split’/ ‘merge’ operators on data cubes. Thus, the proposed Nested Multidimensional Data Model provides great modeling flexibility during the schema design phase and application-oriented restrictiveness during the data analysis phase.

Random sampling is a popular technique for providing fast approximate query answers, especially in data warehouse environments. Compared to other types of synopses, random sampling bears the advantage of retaining the dataset’s dimensionality; it also associates probabilistic error bounds with the query results. Most of the available sampling techniques focus on table-level sampling, that is, they produce a sample of only a single database table. Queries that contain joins over multiple tables cannot be answered with such samples because join results on random samples are often small and skewed. On the contrary, schema-level sampling techniques by design support queries containing joins. In this paper, we introduce Linked Bernoulli Synopses, a schema-level sampling scheme based upon the well-known Join Synopses. Both schemes rely on the idea of maintaining foreign-key integrity in the synopses; they are therefore suited to process queries containing arbitrary foreign-key joins. In contrast to Join Synopses, however, Linked Bernoulli Synopses correlate the sampling processes of the different tables in the database so as to minimize the space overhead, without destroying the uniformity of the individual samples. We also discuss how to compute Linked Bernoulli Synopses which maximize the effective sampling fraction for a given memory budget. The computation of the optimum solution is often computationally prohibitive so that approximate solutions are needed. We propose a simple heuristic approach which is fast and seems to produce close-to-optimum results in practice. We conclude the paper with an evaluation of our methods on both synthetic and real-world datasets.